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Confidence in research findings depends on theory

Published online by Cambridge University Press:  05 February 2024

David Gal Open the ORCID record for David Gal [Opens in a new window] ,
Brian Sternthal  and
Bobby J. Calder
David Gal*
Affiliation:
Department of Marketing, University of Illinois Chicago, Chicago, IL, USA davidgal@uic.edu
Brian Sternthal
Affiliation:
Department of Marketing, Northwestern University, Evanston, IL, USA bst047@kellogg.northwestern.edu; calder@kellogg.northwestern.edu
Bobby J. Calder
Affiliation:
Department of Marketing, Northwestern University, Evanston, IL, USA bst047@kellogg.northwestern.edu; calder@kellogg.northwestern.edu
*
*Corresponding author.
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Abstract

Almaatouq et al. view the purpose of research is to map variable-to-variable relationships (e.g., the effect of X on Y). They also view theory as this mapping of variable-to-variable relationships rather than an explanation of why the relationships occur. However, it is theory as explanation that allows us to reconcile disparate findings and that should guide application.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e41
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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References

Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., … Rahwan, I. (2018). The moral machine experiment. Nature, 563(7729), 59–64.CrossRefGoogle ScholarPubMed
Awad, E., Dsouza, S., Shariff, A., Rahwan, I., & Bonnefon, J. F. (2020). Universals and variations in moral decisions made in 42 countries by 70,000 participants. Proceedings of the National Academy of Sciences of the United States of America, 117(5), 2332–2337.CrossRefGoogle Scholar
Calder, B. J., Brendl, C. M., Tybout, A. M., & Sternthal, B. (2021). Distinguishing constructs from variables in designing research. Journal of Consumer Psychology, 31(1), 188–208.CrossRefGoogle Scholar
Calder, B. J., He, S., & Sternthal, B. (2023). Using theoretical frameworks in behavioral research. Journal of Business Research, forthcoming.CrossRefGoogle Scholar
Calder, B. J., Phillips, L. W., & Tybout, A. M. (1981). Designing research for application. Journal of Consumer Research, 8(2), 197–207.CrossRefGoogle Scholar
Gal, D., & Rucker, D. D. (2022). Experimental validation bias limits the scope and ambition of applied behavioural science. Nature Reviews Psychology, 1(1), 5–6.CrossRefGoogle Scholar
Gal, D., & Rucker, D. D. (2023). Behavioral winter: Disillusionment with applied behavioral science and a path to spring forward. Behavioral and Brain Sciences, forthcoming.CrossRefGoogle Scholar
He, S., & Sternthal, B. (2023). Beyond numbers: An ambiguity–accessibility–applicability–accessibility framework to explain the attraction effect. Journal of Consumer Research, forthcoming.CrossRefGoogle Scholar
Huber, J., Payne, J. W., & Puto, C. P. (1982). Adding asymmetrically dominated alternatives: Violations of regularity and the similarity hypothesis. Journal of Marketing Research, 9, 90–98.Google Scholar